Integration of Machine Learning Algorithms in Predicting Renal Function Decline in Chronic Kidney Disease Patients

The proposed study evaluates the critical role of machine learning (ML) algorithms in predicting renal function decline in individuals with chronic kidney disease (CKD). The crucial need originates from the limitations of traditional methods, which frequently depend on static models that are incapable of adjusting to the dynamic nature of CKD progression. These outdated ways of anticipating decline patterns lack precision, limiting early intervention and personalized patient care. The proposed system employs ML methods to improve prediction accuracy by combining patient-specific data, biomarkers, and clinical factors. The model outperforms other models in projecting renal function decline by using the capability of modern algorithms such as deep learning and ensemble approaches. Through comprehensive analysis with existing systems, the results indicate enhanced accuracy (92%), sensitivity (91%), specificity (88%), and Area Under the Curve (AUC) (92%). The study highlights the essential role of ML integration in boosting predictive capacities, allowing prompt treatments, and increasing outcomes for CKD patients.